With the amount of information, and the complexity of its processing and transmission increasing every day, the need for flexible and, at the same time, cost effective hardware computer solutions is increasing.
Until recently, the computer solutions have been limited to a choice between software oriented general purpose computers (GPCs), such as personal computers and workstations, with or without acceleration boards, and hardware oriented application specific computers (APCs) built into a variety of specialized devices.
A GPC is generally designed with the primary goal of providing an acceptable performance in a wide variety of tasks rather than high performance in specific tasks. The performance of such computers depends upon how well the capabilities of the processing platform match the computational characteristics of the applications. Execution of a particular function in a GPC has to be programmed into a fixed set of instructions based on a fixed arithmetic logic unit (ALU) structure, for which the performance characteristics are further affected by the internal pipeline and the cache type and size associated with the processor. If an application requires more computational power than a GPC can achieve, users are forced to use an APC in which fundamental machine capabilities are designed for a particular class of algorithms. In an APC, functions are executed on hardware designed from standard parts, application specific integrated circuits (ASICs), and other readily available building blocks. Tasks suited to a given APC are performed exceptionally well, but tasks outside the targeted class are usually performed well below an acceptable level of performance, if at all.
However, with availability of the Field Programmable Gate Array (FPGA) from Xilinx Corporation, new possibilities have opened. The FPGA allows the logic circuits in a machine to be reconfigured to the job at hand and, thus, can offer a new mixture of performance and versatility. With the advent of FPGA-based custom computing machines, the potential exists for a user to design, exactly, a special processor for a particular application without having to build different processors for different applications. Machines based on FPGAs can execute with the inherent parallelism of hardware and, for the most part, the absence of much of the overhead of I/O, branch operations or instruction decoding of the GPC.
Currently, FPGAs are not useful alone for complex applications such as, for example, medical imaging. The use of FPGAs for such complex applications immediately requires consideration of data paths, pin limitations, interconnection with memory resources and partition of programs across the chips --in some cases, many chips. Thus, application solutions using FPGAs are presently quite expensive.
New architectural solutions are required to exploit the innate flexibility of the FPGA to make possible the execution of increasingly complex processing operations at lower costs.